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相关概念视频

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

332
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
332

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相关实验视频

Updated: Jul 9, 2025

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
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使用长期短期记忆网络和高斯过程回归的下肢联合扭矩预测.

Mengsi Wang1,2, Zhenlei Chen3, Haoran Zhan1,2

  • 1School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China.

Sensors (Basel, Switzerland)
|December 9, 2023
PubMed
概括

使用机器学习预测下肢关节扭矩是可行的. 表面电肌图 (sEMG) 信号和关节角度使用LSTM和GPR模型准确预测扭矩,显示高相关性和低误差.

关键词:
高斯过程回归的高斯过程回归.电肌图信号 电肌图信号联合扭矩的联合扭矩.长期短期记忆 长期短期记忆机器学习是机器学习.

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科学领域:

  • 生物力学和运动科学 生物力学和运动科学
  • 医疗保健中的机器学习
  • 计算神经科学是一种神经科学.

背景情况:

  • 准确的关节扭矩预测对于生物力学分析和应用至关重要.
  • 传统的方法,如反向动力学和EMG驱动的神经肌肉骨模型有局限性.
  • 机器学习为使用sEMG和动力学数据进行联合扭矩预测提供了一个有希望的替代方案.

研究的目的:

  • 为了预测下肢关节在行走过程中在 sagittal 平面上的扭矩.
  • 评估长期短期记忆 (LSTM) 和高斯过程回归 (GPR) 模型的有效性.
  • 为了利用表面电肌图 (sEMG) 信号特征和关节角度作为输入.

主要方法:

  • 从五个肌肉的sEMG信号中提取了七个特征.
  • 用了三个连接角度作为输入参数.
  • 实现并比较LSTM和GPR机器学习模型.
  • 使用规范化根平均平方误差 (NRMSE) 和皮尔森相关系数 (R) 验证模型性能.

主要成果:

  • 两种LSTM和GPR模型都实现了对联合扭矩的高预测精度.
  • 在这两种模型中,大多数NRMSE值都低于15%.
  • 大多数皮尔森相关系数 (R) 值都超过0.85,表明强有力的线性关系.
  • 大多数确定系数 (R2) 值都超过了0.75,表明模型适合.

结论:

  • 机器学习模型,特别是LSTM和GPR,可以准确地预测走路时下肢关节的扭矩.
  • sEMG信号特征和连接角度是这些预测模型的有效输入.
  • 这些发现支持使用机器学习用于非侵入性关节扭矩估计的可行性.